System and method for lifting 3d representations from monocular images

ABSTRACT

In one embodiment, example systems and methods relate to a manner of generating 3D representations from monocular 2D images. A monocular 2D image is captured by a camera. The 2D image is processed to create one or more feature maps. The features may include depth features, or object labels, for example. Based on the image and the feature map, regions-of-interest corresponding to vehicles in the image are determined. For each region-of-interest a lifting function is applied to the region-of-interest to determine values such as height and width, camera distance, and rotation. The determined values are used to create an eight-point box that is a 3D representation of the vehicle depicted by the region-of-interest. The 3D representation can be used for a variety of purposes such as route planning, object avoidance, or as training data, for example.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/704,033 and filed on Nov. 15, 2018. The disclosure of whichis hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter described herein relates, in general, to systems andmethods for lifting 3D representations from monocular images, and, inparticular, to training a lifting function to generate 3Drepresentations from regions-of-interest in monocular images.

BACKGROUND

Currently, may vehicles, such as autonomous cars, include multiplecameras that capture images of the surroundings of a vehicle. Forexample, an autonomous vehicle may have a camera that captures theenvironment in the front of the vehicle, a camera that captures theenvironment in the rear of the vehicle, a camera that captures theenvironment to the left of the vehicle, and a camera that captures theenvironment to the right of the vehicle.

As may be appreciated, the images captured by such cameras aretwo-dimensional images, and more specifically monocular images. Whilesuch monocular images can provide useful information to the autonomousvehicle, in some scenarios it may be desirable to extract threedimensional information about one or more objects in the images.

For example, a two-dimensional image may show one or more objects suchas other vehicles in front of the vehicle. In order for the vehicle todetermine the best way to avoid the vehicles shown in thetwo-dimensional image, it would be useful for the vehicle to havethree-dimensional information about the vehicles shown in the image suchas their dimensions and orientation, for example.

Furthermore, extracting three-dimensional information fromtwo-dimensional images may be useful for a variety of other fields andtechnologies that commonly use two-dimensional images. These may includesecurity (e.g., generating three-dimensional representations ofintruders or other objects captured in security footage), film making,and the generation of training data.

SUMMARY

In one embodiment, example systems and methods relate to a manner ofgenerating 3D representations from monocular 2D images. A monocular 2Dimage is captured by a camera. The 2D image is processed to create oneor more feature maps. The features may include depth features, or objectlabels, for example. Based on the image and the feature map,regions-of-interest corresponding to vehicles in the image aredetermined. For each region-of-interest a lifting function is applied tothe region-of-interest to determine values such as height and width,camera distance, and rotation. The determined values are used to createan eight-point box that is a 3D representation of the vehicle depictedby the region-of-interest. The 3D representation can be used for avariety of purposes such as route planning, object avoidance, or astraining data, for example.

In one embodiment, a system for generating 3D representations fromimages is provided. The system includes one or more processors and amemory communicably coupled to the one or more processors. The memorystoring an image module including instructions that when executed by theone or more processors cause the one or more processors to capture animage. The memory further storing a feature module includinginstructions that when executed by the one or more processors cause theone or more processors to generate one or more feature maps for thecaptured image. The memory further storing a feature module includinginstructions that when executed by the one or more processors cause theone or more processors to generate one or more feature maps for thecaptured image. The memory further storing a region-of-interest moduleincluding instructions that when executed by the one or more processorscause the one or more processors to determine one or moreregions-of-interests in the captured image based in part on the one ormore feature maps. The memory further storing a lifting module includinginstructions that when executed by the one or more processors cause theone or more processors to for each region-of-interest, generate a 3Drepresentation based on the region-of-interest and the one or morefeature maps.

Embodiments may include some or all of the following features. Eachregion-of-interest may correspond to a vehicle. The memory may furtherstore a training module including instructions that when executed by theone or more processors cause the one or more processors to generatetraining data from the generated 3D representations. Each 3Drepresentation may be a 3D box. Each 3D representation may be generatedusing a lifting function. The memory may further store a loss moduleincluding instructions that when executed by the one or more processorscause the one or more processors to calculate a loss for one or more the3D representations. The loss module may further include instructionsthat when executed by the one or more processors cause the one or moreprocessors to train the lifting function using the calculated loss.Generating a 3D representation based on the region-of-interest and theone or more feature maps may include: calculating a height and width forthe region-of-interest from the one or more feature maps; calculating ageometric center for the region-of-interest from the one or more featuremaps; calculating a camera distance for the region-of-interest from theone or more feature maps; calculating a rotation for theregion-of-interest from the one or more feature maps; and generating the3D representation of the region-of-interest from one or more of thecalculated height and width, the calculated geometric center, thecalculated camera distance, and the calculated rotation.

In one embodiment, a method for generating 3D representations fromimages is provided. The method includes: receiving an image and one ormore feature maps for the image; determining one or moreregions-of-interest in the image based in part of the image and the oneor more feature maps; for each region-of-interest, generating a 3Drepresentation based on the region-of-interest and the one or morefeature maps using a lifting function; calculating a loss for one ormore of the generated 3D representations; and training the liftingfunction using the calculated loss.

Embodiments may include some or all of the following features. Eachregion-of-interest may correspond to a vehicle. The method may furtherinclude generating training data from the generated 3D representation.Each 3D representation may be a 3D box. Generating a 3D representationbased on the region-of-interest and the one or more feature maps usingthe lifting function may include: calculating a height and width for theregion-of-interest from the one or more feature maps; calculating ageometric center for the region-of-interest from the one or more featuremaps; calculating a camera distance for the region-of-interest from theone or more feature maps; calculating a rotation for theregion-of-interest from the one or more feature maps; and generating the3D representation of the region-of-interest from one or more of thecalculated height and width, the calculated geometric center, thecalculated camera distance, and the calculated rotation. The image maybe a 2D monocular image.

In one embodiment, a non-transitory computer-readable medium forgenerating 3D representations from images is provided. Thenon-transitory computer-readable medium includes instructions that whenexecuted by one or more processors cause the one or more processors to:capture an image; generate one or more feature maps for the capturedimage; determine one or more regions-of-interest in the image based inpart of the image and the one or more feature maps; and for eachregion-of-interest, generate a 3D representation based on theregion-of-interest and the one or more feature maps using a liftingfunction.

Embodiments may include some or all of the following features. Eachregion-of-interest may correspond to a vehicle. Each 3D representationmay be a 3D box. Generating a 3D representation based on theregion-of-interest and the one or more feature maps may include:calculating a height and width for the region-of-interest from the oneor more feature maps; calculating a geometric center for theregion-of-interest from the one or more feature maps; calculating acamera distance for the region-of-interest from the one or more featuremaps; calculating a rotation for the region-of-interest from the one ormore feature maps; and generating the 3D representation of theregion-of-interest from one or more of the calculated height and width,the calculated geometric center, the calculated camera distance, and thecalculated rotation. The image may be a 3D monocular image.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various systems, methods, andother embodiments of the disclosure. It will be appreciated that theillustrated element boundaries (e.g., boxes, groups of boxes, or othershapes) in the figures represent one embodiment of the boundaries. Insome embodiments, one element may be designed as multiple elements ormultiple elements may be designed as one element. In some embodiments,an element shown as an internal component of another element may beimplemented as an external component and vice versa. Furthermore,elements may not be drawn to scale.

FIG. 1 illustrates one embodiment of a 3D representation system that isassociated with generating training data including labeled syntheticimages.

FIG. 2 illustrates one example of a 3D representation system as embodiedherein.

FIG. 3 illustrates a flowchart of a method that is associated withgenerating 3D representations from regions-of-interest in images.

FIG. 4 illustrates a flowchart of a method that is associated withtraining a lifting function using calculated loss.

FIG. 5 illustrates an example image including a region-of-interest.

FIG. 6 illustrates an example image including a 3D representationcorresponding to a region-of-interest.

DETAILED DESCRIPTION

Systems, methods, and other embodiments associated with generatingthree-dimensional (“3D”) representations of regions-of-interest inmonocular two-dimensional (“2D”) images are disclosed. As describedabove, vehicles such as the vehicle 100 of FIG. 1 frequently includecameras that generate 2D monocular images. These images may be used forpurposes of hazard avoidance or navigation. Typically, the vehicle 100processes the images to identify regions-of-interest in the images thatmay correspond to hazards such as other vehicles and pedestrians, or toidentify regions-of-interest that may correspond to street signs ortraffic lights. While such regions-of-interest provide usefulinformation, because they are 2D rather than 3D, they are less usefulthan the equivalent 3D representation.

Accordingly, to help generate 3D representations of regions-of-interestfrom 2D monocular images, a 3D representation system is provided. Insome embodiments, the 3D representation system uses a lifting functionto generate 3D representations from 2D regions-of-interest in a 2Dmonocular image. The lifting function may generate a 3D bounding box fora region-of-interest that may express the dimensions and orientation ofthe particular object depicted in the region-of-interest. The liftingfunction may be trained using a loss function that calculates the lossbetween the 3D bounding box generated for a region-of-interest and aknown ground truth box for the region-of-interest. The 3Drepresentations may be used for a variety of purposes such as hazarddetection and avoidance in the vehicle 100, or for generating trainingdata to train one or more classifiers, for example.

The vehicle 100 also includes various elements. It will be understoodthat in various embodiments it may not be necessary for the vehicle 100to have all of the elements shown in FIG. 1. The vehicle 100 can haveany combination of the various elements shown in FIG. 1. Further, thevehicle 100 can have additional elements to those shown in FIG. 1. Insome arrangements, the vehicle 100 may be implemented without one ormore of the elements shown in FIG. 1. While the various elements areshown as being located within the vehicle 100 in FIG. 1, it will beunderstood that one or more of these elements can be located external tothe vehicle 100. Further, the elements shown may be physically separatedby large distances.

Some of the possible elements of the vehicle 100 are shown in FIG. 1 andwill be described along with subsequent figures. However, a descriptionof many of the elements in FIG. 1 will be provided after the discussionof FIGS. 2-5 for purposes of brevity of this description. Additionally,it will be appreciated that for simplicity and clarity of illustration,where appropriate, reference numerals have been repeated among thedifferent figures to indicate corresponding or analogous elements. Inaddition, the discussion outlines numerous specific details to provide athorough understanding of the embodiments described herein. Those ofskill in the art, however, will understand that the embodimentsdescribed herein may be practiced using various combinations of theseelements.

In either case, the vehicle 100 includes a 3D representation system 170that is implemented to perform methods and other functions as disclosedherein relating to determining optimal paths for the vehicle 100. Thenoted functions and methods will become more apparent with a furtherdiscussion of the figures.

With reference to FIG. 2, one embodiment of the 3D representation system170 of FIG. 1 is further illustrated. The 3D representation system 170is shown as including a processor 110 from the vehicle 100 of FIG. 1.Accordingly, the processor 110 may be a part of the 3D representationsystem 170, the 3D representation system 170 may include a separateprocessor from the processor 110 of the vehicle 100 or the 3Drepresentation system 170 may access the processor 110 through a databus or another communication path. It should be appreciated, that whilethe 3D representation system 170 is illustrated as being a singlecontained system, in various embodiments, the 3D representation system170 is a distributed system that is comprised of components that can beprovided as a centralized server, a cloud-based service, and so on.Moreover, the 3D representation system 170 described herein is notlimited to vehicle-based implementations, but may be implemented usingany general purpose computing device.

In one embodiment, the 3D representation system 170 includes a memory210 that stores an image module 220, a feature module 223, aregion-of-interest module 225, a lifting module 227, a loss module 229,and a training module 231. More or fewer modules may be supported. Thememory 210 is a random-access memory (RAM), read-only memory (ROM), ahard-disk drive, a flash memory, or other suitable memory for storingthe modules 220, 223, 225, 227, 229, and 231. The modules 220, 223, 225,227, 229, and 231 are, for example, computer-readable instructions thatwhen executed by the processor 110 cause the processor 110 to performthe various functions disclosed herein. Moreover, as previously noted,in various embodiments, one or more aspects of the 3D representationsystem 170 are implemented as cloud-based services, and so on. Thus, oneor more modules of the 3D representation system 170 may be locatedremotely from other components and may be implemented in a distributedmanner.

Furthermore, in one embodiment, the 3D representation system 170includes the database 240. The database 240 is, in one embodiment, anelectronic data structure stored in the memory 210 or another data storeand that is configured with routines that can be executed by theprocessor 110 for analyzing stored data, providing stored data,organizing stored data, and so on. Thus, in one embodiment, the database240 stores data used by the modules 220, 223, 225, 227, 229, and 231 inexecuting various functions. In one embodiment, the database 240includes an image 280 along with, for example, other information that isused and/or generated by the modules 220, 223, 225, 227, 229, and 231such as one or more features map 285, regions-of-interest 287, 3Drepresentations 291, training data 295, and loss 293. Of course, infurther embodiments, the various information may be stored within thememory 210 or another suitable location.

The image module 220 may be configured to capture one or more images280. The image 280 may be a monocular image 280 and may be captured bythe image module 220 using one or more sensors of the sensor system 120of the vehicle 100, such as the camera 126. Other types of sensors maybe supported. The captured image 280 may be an RGB image 280, althoughother image types and/or formats may be supported. Depending on theembodiment, the image module 220 may receive the image 280 from a camera126 associated with a different vehicle 100, or from a differentcomputing device such as a smart phone, for example.

The feature module 223 may be configured to generate one or more featuremaps 285 for the image 280. Depending on the embodiment, each featuremap 285 may be a pixel-level feature map 285, and may associate labelswith each pixel of the image 280. In one example, the feature map 285 isa depth map, and may include an estimated depth for each pixel of theimage 280. The depth estimated for each pixel may be an estimate of thedistance from the camera that took the image 280 to the object orfeature of the image 280 that includes the pixel. In another example,the feature map 285 may identify the object or feature of the image 280that is associated with a pixel of the image 280. For example, thefeature map 285 may identify pixels of the image 280 that are associatedwith objects such as tires, faces, animals, words, etc. Other types offeature map 285 may be supported. Any method or technique for generatingfeature maps 285 for an image 280 may be used.

The region-of-interest module 225 may determine one or moreregions-of-interest 287 in the image 280. The region-of-interest module225 may determine the one or more regions-of-interest 287 using thepixels of the image 280 and one or more of the feature maps 285generated by the feature module 223. The regions-of-interest 287 may beregions of the image 280 that depict vehicles. Other types of objectsmay be depicted in each region-of-interest 287.

Each region-of-interest 287 generated or determined by theregion-of-interest module 225 may include a section of pixels from theimage 280. In addition, each pixel of the region-of-interest 287 may beassociated with information from the one or more feature maps 285, suchas depth information. In some implementations, the region-of-interestmodule 225 may use an application such as RoIAlign to determine theregions-of-interest 287 in the image 280. However, any method ortechnique known in the art for determining regions-of-interest 287 maybe used.

The lifting module 227 may be configured to generate 3D representations291 of the determined regions-of-interest 287. In some embodiments, thelifting module 227 may generate a 3D representation 291 using a liftingfunction or a lifting map that internally regresses components of the 3Drepresentation 291 from the region-of-interest 287. Depending on theembodiment, the 3D representation 291 of the region-of-interest 287 maybe a box that is made up of eight ordered 3D points. The components ofthe 3D representation lifted from the region-of-interest 287 mayinclude, but are not limited to, height and width, geometric center,camera distance, and rotation.

More specifically, the lifting function may be a mapping F:

→

^(8×3) from a 2D region-of-interest X to a 3D box B:={B₁, . . . , B₈} ofeight ordered 3D points (i.e., the 3D representation 291). Depending onthe embodiment, the rotation of the 3D representation 291 may be encodedas a 4D quaternion and the translation as a projected 2D object centroidtogether with the associated depth.

Given the region-of-interest 287 X, the lifting function F(X) may runthe application RoIAlign at the position of the region-of-interest 287in the image 280 (including feature maps 285), followed by separateprediction heads to recover the various components of the 3Drepresentation 291 in the region-of-interest 287. These components mayinclude, but are not limited to, rotation q, 2D centroid (i.e.,geometric center), depth z (i.e., camera depth), and metric extends (w,h, l). From these components, the lifting module 227 may construct theeight corners of the 3D box B_(i) (i.e., the 3D representation 291 as:

$B_{i}:={{q \cdot \begin{pmatrix}{{\pm w}/2} \\{{\pm h}/2} \\{{\pm l}/2}\end{pmatrix} \cdot q^{- 1}} + {K^{- 1}\begin{pmatrix}{x \cdot z} \\{y \cdot z} \\z\end{pmatrix}}}$

In the above formula, K⁻¹ is the inverse camera intrinsics. The liftingmodule 227 may build the points B_(i) in a defined order to preserveabsolute orientation. Any order may be used. The lifting module 227 mayuse the above formula to generate 3D representations 291 from each ofthe regions-of-interest 287.

The loss module 229 may be configured to calculate a loss 293 for each3D representation 291. As may be appreciated, when estimating a 3Drepresentation 291 from a monocular image 280, small deviations in pixelspace can introduce large errors in the 3D representation 291.Penalizing each term of the 3D representation 291 to correct for theseerrors may lead to volatile optimization and may be prone to suboptimallocal minima. To account for these errors, the loss module 229 maycalculate the loss 293 for a 3D representation 291. The calculated loss293 may be used by the loss module 229 to adjust the 3D representation291, or to train the lifting function used by the lifting module 227,for example.

In some embodiments, the loss module 229 may calculate the loss for a 3Drepresentation 291 by comparing the box B with a ground truth boxB*:={B*₁, . . . , B*₈} associated with the same region-of-interest 287 Xthat the box B was generated from. The loss module 229 may calculate theloss 293 as the mean over the 8 corner distances in metric spaceaccording to the following formula:

${L( {{F(X)},B^{*}} )} = {\frac{1}{8}{\sum\limits_{i \in {({1\mspace{14mu} \ldots \mspace{14mu} 8})}}^{\;}\; {{{F(X)}_{i} - B_{i}^{*}}}}}$

The lifting module 220 may be further configured to predict a shape ormesh for the 3D representations 291. In order to train the liftingmodule 220 to predict the shape for a 3D representation 291 from theassociated region-of-interest 287 the lifting module 220 may, for agenerated shape s for a region-of-interest 287, compute the loss 293between the computed shape s and the known ground truth shape s* usingthe following formula:

L _(shape)(s,s*)=arcos(2

s,s*

²1)

The lifting module 220 may be further configured to generate a texturefor each 3D representation 291. As may be appreciated, the 3Drepresentation 291, specifically the 8 point box, includes the absolutescale and 6D pose. Accordingly, in some embodiments, the lifting module220 may generate a texture for the 3D representation 291 by projectingeach camera-facing vertex of the predicted shape or mesh into the image280, and assigning the corresponding pixel values of the image 280 tothe projected vertices. To account for the non-camera facing vertices,the lifting module 220 may assign pixels to these vertices based on thesymmetry of the 3D representation 291, for example.

The training module 231 may be configured to generate training data 295from the 3D representations 291 generated by the lifting module 227. Insome implementations, the training data 295 may be labeled training data295. Labeled training data 295 may include a label that describes eachof the 3D representations 291 extracted or generated from theregions-of-interest 287 in the image 280. Depending on the embodiment,the labels for each of the 3D representations 291 may be taken from theone or more feature maps 285 associated with the image 280, or may bemanually provided by a human labeler, for example.

The training module 231 may use the training data 295 to train a varietyof classifiers, or other learning functions. For example, if thetraining data 295 includes images 280 with 3D representations 291 ofvehicles, the training data 295 may be used to train a classifier torecognize vehicles. In another example, if the training data 295includes images 280 with 3D representations 291 of people, the trainingdata 295 may be used to train a classifier to recognize humans.

Additional aspects of generating 3D representations 291 from 2D images180 will be discussed in relation to FIG. 3. FIG. 3 illustrates aflowchart of a method 300 that is associated with generating 3Drepresentations 291 from regions-of-interest 287 in images 280. Themethod 300 will be discussed from the perspective of the 3Drepresentation system 170 of FIGS. 1 and 2. While the method 300 isdiscussed in combination with the 3D representation system 170, itshould be appreciated that the method 300 is not limited to beingimplemented within the 3D representation system 170 but is instead oneexample of a system that may implement the method 300.

At 310, the image module 220 captures an image 280. The image module 220may capture the image 280 using a camera 126 associated with the vehicle100. The image 280 may be a monocular image 280. The image 280 maydepict a scene in front of the vehicle 100 and may include one or moreother vehicles. Continuing to FIG. 5, an example image 510 isillustrated. As shown, the image 510 depicts a vehicle 505.

Returning to FIG. 3, at 320, the feature module 223 generates one ormore feature maps 285. The feature maps 285 may include a depth map thatindicates a depth for each pixel of the image 280. Other types offeature maps 285 may be supported.

At 330, the region-of-interest module 225 determines one or moreregions-of-interest 287 in the image 280. The region-of-interest module225 may determine the one or more regions-of-interest 287 using the oneor more feature maps 285. Depending on the embodiment, eachregion-of-interest 287 may be a region of pixels in the image 280 andmay depict a vehicle 100. Any method for determining regions-of-interest287 in an image 280 may be used. Returning to FIG. 5, theregion-of-interest module 225 has determined a region-of-interest 515corresponding to the vehicle 505 depicted in the image 510. Theregion-of-interest 515 is illustrated in the image 510 using a dashedoval.

Returning to FIG. 3, at 340, the lifting module 227 generates a 3Drepresentation 291 for each region-of-interest 287. The lifting module227 may generate each 3D representation 291 using a lifting functionthat was previously trained to generate 3D representations 291 fromregions-of-interest 287. Each 3D representation 291 may be an 8 pointbox and may have been generated by the lifting module 227 by calculatingone or more of a height and width for the associated region-of-interest287, a geometric center for the associated region-of-interest 287, acamera distance for the associated region-of-interest 287, and arotation for the associated region-of-interest 287.

In addition, in some embodiments, the lifting module 227 may generate amesh shape and a texture for each 3D representation 291. With respect tothe mesh shape, the lifting module 227 may have been trained todetermine shapes for objects such as vehicles from regions-of-interest287 in images 280. With respect to the texture, the lifting module 227may project some or all of the pixels of the region-of-interest onto theshape of the 3D representation 291. Any method for determining shapesand textures for 3D representations 291 may be used.

Continuing to FIG. 6, the lifting module 225 has generated a 3Drepresentation 615 corresponding to the vehicle 505 (and theregion-of-interest 515) depicted in the image 510. The 3D representation617 is illustrated in the image 510 using the dashed 3D box. As shown,the box has eight points.

Returning to FIG. 3, at 350, the training module 231 may generatetraining data 295 from the generated 3D representations 291 for each ofthe regions-of-interest 287. The training data 295 may be used to trainone or more classifiers, for example. The 3D representations 291 in thetraining data 295 may be labeled. The training module 231 may generatethe labels for each 3D representation 291 from information from the oneor more feature maps 285 associated with the 3D representation 291.Other methods for labeling training data 295 may be used.

Additional aspects of generating 3D representations fromregions-of-interests in 2 d monocular images will be discussed inrelation to FIG. 4. FIG. 4 illustrates a flowchart of a method 400 thatis associated with training a lifting function using calculated loss.The method 400 will be discussed from the perspective of the 3Drepresentation system 170 of FIGS. 1 and 2. While the method 400 isdiscussed in combination with the 3D representation system 170, itshould be appreciated that the method 400 is not limited to beingimplemented within the 3D representation system 170 but is instead oneexample of a system that may implement the method 400.

At 410, the region-of-interest module 225 receives an image 280 and oneor more feature maps 285. The image 280 may be a 2D monocular image 280and may have been captured by a camera associated with a vehicle 100.The image 280 may be associated with one or more feature map 285. Theone or more feature maps 285 may have been generated by the featuremodule 223. Each feature map 285 may identify objects in the image 280,or may provide depth information for one or more pixels of the image280.

At 420, the region-of-interest module 225 determines one or moreregions-of-interest 287 in the image 280. The region-of-interest module225 may determine the one or more regions-of-interest 287 in the image280 based on the associated feature maps 285. Depending on theimplementation, each region-of-interest may be associated with a groundtruth box.

At 430, the lifting module 227 generates a 3D representation 291 foreach region-of-interest 287. The lifting module 227 may generate each 3Drepresentation 291 using a lifting function. Each 3D representation 291may be an 8 point box and may have been generated by the lifting module227 by calculating one or more of a height and width for the associatedregion-of-interest 287, a geometric center for the associatedregion-of-interest 287, a camera distance for the associatedregion-of-interest 287, and a rotation for the associatedregion-of-interest 287.

At 440, the loss module 229 calculates a loss 293 for one or more of thegenerated 3D representations 291. Depending on the embodiment, the lossmodule 229 may calculate the loss 293 for a generated 3D representation291 by comparing the box generated at 430 with the ground truth boxassociated with the region-of-interest 287. The particular formula usedby the loss module 229 is described in paragraph [0034]. Other methodsof formulas for calculating the loss 293 may be used. Generally, thecloser or more similar the generated box and the ground truth box, thesmaller the calculated loss 293.

At 450, the lifting module 227 may train the lifting function using thelosses 293 generated for each of the generated 3D representations 291.Any method or technique for training a function may be used.

FIG. 1 will now be discussed in full detail as an example environmentwithin which the system and methods disclosed herein may operate. Insome instances, the vehicle 100 is configured to switch selectivelybetween an autonomous mode, one or more semi-autonomous operationalmodes, and/or a manual mode. Such switching can be implemented in asuitable manner, now known or later developed. “Manual mode” means thatall of or a majority of the navigation and/or maneuvering of the vehicleis performed according to inputs received from a user (e.g., humandriver). In one or more arrangements, the vehicle 100 can be aconventional vehicle that is configured to operate in only a manualmode.

In one or more embodiments, the vehicle 100 is an autonomous vehicle. Asused herein, “autonomous vehicle” refers to a vehicle that operates inan autonomous mode. “Autonomous mode” refers to navigating and/ormaneuvering the vehicle 100 along a travel route using one or morecomputing systems to control the vehicle 100 with minimal or no inputfrom a human driver. In one or more embodiments, the vehicle 100 ishighly automated or completely automated. In one embodiment, the vehicle100 is configured with one or more semi-autonomous operational modes inwhich one or more computing systems perform a portion of the navigationand/or maneuvering of the vehicle along a travel route, and a vehicleoperator (i.e., driver) provides inputs to the vehicle to perform aportion of the navigation and/or maneuvering of the vehicle 100 along atravel route.

The vehicle 100 can include one or more processors 110. In one or morearrangements, the processor(s) 110 can be a main processor of thevehicle 100. For instance, the processor(s) 110 can be an electroniccontrol unit (ECU). The vehicle 100 can include one or more data stores115 for storing one or more types of data. The data store 115 caninclude volatile and/or non-volatile memory. Examples of suitable datastores 115 include RAM (Random Access Memory), flash memory, ROM (ReadOnly Memory), PROM (Programmable Read-Only Memory), EPROM (ErasableProgrammable Read-Only Memory), EEPROM (Electrically ErasableProgrammable Read-Only Memory), registers, magnetic disks, opticaldisks, hard drives, or any other suitable storage medium, or anycombination thereof. The data store 115 can be a component of theprocessor(s) 110, or the data store 115 can be operatively connected tothe processor(s) 110 for use thereby. The term “operatively connected,”as used throughout this description, can include direct or indirectconnections, including connections without direct physical contact.

In one or more arrangements, the one or more data stores 115 can includemap data 116. The map data 116 can include maps of one or moregeographic areas. In some instances, the map data 116 can includeinformation or data on roads, traffic control devices, road markings,structures, features, and/or landmarks in the one or more geographicareas. The map data 116 can be in any suitable form. In some instances,the map data 116 can include aerial views of an area. In some instances,the map data 116 can include ground views of an area, including360-degree ground views. The map data 116 can include measurements,dimensions, distances, and/or information for one or more items includedin the map data 116 and/or relative to other items included in the mapdata 116. The map data 116 can include a digital map with informationabout road geometry. The map data 116 can be high quality and/or highlydetailed.

In one or more arrangements, the map data 116 can include one or moreterrain maps 117. The terrain map(s) 117 can include information aboutthe ground, terrain, roads, surfaces, and/or other features of one ormore geographic areas. The terrain map(s) 117 can include elevation datain the one or more geographic areas. The map data 116 can be highquality and/or highly detailed. The terrain map(s) 117 can define one ormore ground surfaces, which can include paved roads, unpaved roads,land, and other things that define a ground surface.

In one or more arrangements, the map data 116 can include one or morestatic obstacle maps 118. The static obstacle map(s) 118 can includeinformation about one or more static obstacles located within one ormore geographic areas. A “static obstacle” is a physical object whoseposition does not change or substantially change over a period of timeand/or whose size does not change or substantially change over a periodof time. Examples of static obstacles include trees, buildings, curbs,fences, railings, medians, utility poles, statues, monuments, signs,benches, furniture, mailboxes, large rocks, hills. The static obstaclescan be objects that extend above ground level. The one or more staticobstacles included in the static obstacle map(s) 118 can have locationdata, size data, dimension data, material data, and/or other dataassociated with it. The static obstacle map(s) 118 can includemeasurements, dimensions, distances, and/or information for one or morestatic obstacles. The static obstacle map(s) 118 can be high qualityand/or highly detailed. The static obstacle map(s) 118 can be updated toreflect changes within a mapped area.

The one or more data stores 115 can include sensor data 119. In thiscontext, “sensor data” means any information about the sensors that thevehicle 100 is equipped with, including the capabilities and otherinformation about such sensors. As will be explained below, the vehicle100 can include the sensor system 120. The sensor data 119 can relate toone or more sensors of the sensor system 120. As an example, in one ormore arrangements, the sensor data 119 can include information on one ormore LIDAR sensors 124 of the sensor system 120.

In some instances, at least a portion of the map data 116 and/or thesensor data 119 can be located in one or more data stores 115 locatedonboard the vehicle 100. Alternatively, or in addition, at least aportion of the map data 116 and/or the sensor data 119 can be located inone or more data stores 115 that are located remotely from the vehicle100.

As noted above, the vehicle 100 can include the sensor system 120. Thesensor system 120 can include one or more sensors. “Sensor” means anydevice, component and/or system that can detect, and/or sense something.The one or more sensors can be configured to detect, and/or sense inreal-time. As used herein, the term “real-time” means a level ofprocessing responsiveness that a user or system senses as sufficientlyimmediate for a particular process or determination to be made, or thatenables the processor to keep up with some external process.

In arrangements in which the sensor system 120 includes a plurality ofsensors, the sensors can work independently from each other.Alternatively, two or more of the sensors can work in combination witheach other. In such case, the two or more sensors can form a sensornetwork. The sensor system 120 and/or the one or more sensors can beoperatively connected to the processor(s) 110, the data store(s) 115,and/or another element of the vehicle 100 (including any of the elementsshown in FIG. 1). The sensor system 120 can acquire data of at least aportion of the external environment of the vehicle 100 (e.g., nearbyvehicles).

The sensor system 120 can include any suitable type of sensor. Variousexamples of different types of sensors will be described herein.However, it will be understood that the embodiments are not limited tothe particular sensors described. The sensor system 120 can include oneor more vehicle sensors 121. The vehicle sensor(s) 121 can detect,determine, and/or sense information about the vehicle 100 itself. In oneor more arrangements, the vehicle sensor(s) 121 can be configured todetect, and/or sense position and orientation changes of the vehicle100, such as, for example, based on inertial acceleration. In one ormore arrangements, the vehicle sensor(s) 121 can include one or moreaccelerometers, one or more gyroscopes, an inertial measurement unit(IMU), a dead-reckoning system, a global navigation satellite system(GNSS), a global positioning system (GPS), a navigation system 147,and/or other suitable sensors. The vehicle sensor(s) 121 can beconfigured to detect, and/or sense one or more characteristics of thevehicle 100. In one or more arrangements, the vehicle sensor(s) 121 caninclude a speedometer to determine a current speed of the vehicle 100.

Alternatively, or in addition, the sensor system 120 can include one ormore environment sensors 122 configured to acquire, and/or sense drivingenvironment data. “Driving environment data” includes data orinformation about the external environment in which an autonomousvehicle is located or one or more portions thereof. For example, the oneor more environment sensors 122 can be configured to detect, quantifyand/or sense obstacles in at least a portion of the external environmentof the vehicle 100 and/or information/data about such obstacles. Suchobstacles may be stationary objects and/or dynamic objects. The one ormore environment sensors 122 can be configured to detect, measure,quantify and/or sense other things in the external environment of thevehicle 100, such as, for example, lane markers, signs, traffic lights,traffic signs, lane lines, crosswalks, curbs proximate the vehicle 100,off-road objects, etc.

Various examples of sensors of the sensor system 120 will be describedherein. The example sensors may be part of the one or more environmentsensors 122 and/or the one or more vehicle sensors 121. However, it willbe understood that the embodiments are not limited to the particularsensors described.

As an example, in one or more arrangements, the sensor system 120 caninclude one or more radar sensors 123, one or more LIDAR sensors 124,one or more sonar sensors 125, and/or one or more cameras 126. In one ormore arrangements, the one or more cameras 126 can be high dynamic range(HDR) cameras or infrared (IR) cameras.

The vehicle 100 can include an input system 130. An “input system”includes any device, component, system, element or arrangement or groupsthereof that enable information/data to be entered into a machine. Theinput system 130 can receive an input from a vehicle passenger (e.g., adriver or a passenger). The vehicle 100 can include an output system135. An “output system” includes any device, component, or arrangementor groups thereof that enable information/data to be presented to avehicle passenger (e.g., a person, a vehicle passenger, etc.).

The vehicle 100 can include one or more vehicle systems 140. Variousexamples of the one or more vehicle systems 140 are shown in FIG. 1.However, the vehicle 100 can include more, fewer, or different vehiclesystems. It should be appreciated that although particular vehiclesystems are separately defined, each or any of the systems or portionsthereof may be otherwise combined or segregated via hardware and/orsoftware within the vehicle 100. The vehicle 100 can include apropulsion system 141, a braking system 142, a steering system 143,throttle system 144, a transmission system 145, a signaling system 146,and/or a navigation system 147. Each of these systems can include one ormore devices, components, and/or a combination thereof, now known orlater developed.

The navigation system 147 can include one or more devices, applications,and/or combinations thereof, now known or later developed, configured todetermine the geographic location of the vehicle 100 and/or to determinea travel route for the vehicle 100. The navigation system 147 caninclude one or more mapping applications to determine a travel route forthe vehicle 100. The navigation system 147 can include a globalpositioning system, a local positioning system or a geolocation system.

The processor(s) 110, the 3D representation system 170, and/or theautonomous driving module(s) 160 can be operatively connected tocommunicate with the various vehicle systems 140 and/or individualcomponents thereof. For example, returning to FIG. 1, the processor(s)110 and/or the autonomous driving module(s) 160 can be in communicationto send and/or receive information from the various vehicle systems 140to control the movement, speed, maneuvering, heading, direction, etc. ofthe vehicle 100. The processor(s) 110, the 3D representation system 170,and/or the autonomous driving module(s) 160 may control some or all ofthese vehicle systems 140 and, thus, may be partially or fullyautonomous.

The processor(s) 110, the 3D representation system 170, and/or theautonomous driving module(s) 160 can be operatively connected tocommunicate with the various vehicle systems 140 and/or individualcomponents thereof. For example, returning to FIG. 1, the processor(s)110, the 3D representation system 170, and/or the autonomous drivingmodule(s) 160 can be in communication to send and/or receive informationfrom the various vehicle systems 140 to control the movement, speed,maneuvering, heading, direction, etc. of the vehicle 100. Theprocessor(s) 110, the 3D representation system 170, and/or theautonomous driving module(s) 160 may control some or all of thesevehicle systems 140.

The processor(s) 110, the 3D representation system 170, and/or theautonomous driving module(s) 160 may be operable to control thenavigation and/or maneuvering of the vehicle 100 by controlling one ormore of the vehicle systems 140 and/or components thereof. For instance,when operating in an autonomous mode, the processor(s) 110, the 3Drepresentation system 170, and/or the autonomous driving module(s) 160can control the direction and/or speed of the vehicle 100. Theprocessor(s) 110, the 3D representation system 170, and/or theautonomous driving module(s) 160 can cause the vehicle 100 to accelerate(e.g., by increasing the supply of fuel provided to the engine),decelerate (e.g., by decreasing the supply of fuel to the engine and/orby applying brakes) and/or change direction (e.g., by turning the fronttwo wheels). As used herein, “cause” or “causing” means to make, force,compel, direct, command, instruct, and/or enable an event or action tooccur or at least be in a state where such event or action may occur,either in a direct or indirect manner.

The vehicle 100 can include one or more actuators 150. The actuators 150can be any element or combination of elements operable to modify, adjustand/or alter one or more of the vehicle systems 140 or componentsthereof to responsive to receiving signals or other inputs from theprocessor(s) 110 and/or the autonomous driving module(s) 160. Anysuitable actuator can be used. For instance, the one or more actuators150 can include motors, pneumatic actuators, hydraulic pistons, relays,solenoids, and/or piezoelectric actuators, just to name a fewpossibilities.

The vehicle 100 can include one or more modules, at least some of whichare described herein. The modules can be implemented ascomputer-readable program code that, when executed by a processor 110,implement one or more of the various processes described herein. One ormore of the modules can be a component of the processor(s) 110, or oneor more of the modules can be executed on and/or distributed among otherprocessing systems to which the processor(s) 110 is operativelyconnected. The modules can include instructions (e.g., program logic)executable by one or more processor(s) 110. Alternatively, or inaddition, one or more data store 115 may contain such instructions.

In one or more arrangements, one or more of the modules described hereincan include artificial or computational intelligence elements, e.g.,neural network, fuzzy logic or other machine learning algorithms.Further, in one or more arrangements, one or more of the modules can bedistributed among a plurality of the modules described herein. In one ormore arrangements, two or more of the modules described herein can becombined into a single module.

The vehicle 100 can include one or more autonomous driving modules 160.The autonomous driving module(s) 160 can be configured to receive datafrom the sensor system 120 and/or any other type of system capable ofcapturing information relating to the vehicle 100 and/or the externalenvironment of the vehicle 100. In one or more arrangements, theautonomous driving module(s) 160 can use such data to generate one ormore driving scene models. The autonomous driving module(s) 160 candetermine position and velocity of the vehicle 100. The autonomousdriving module(s) 160 can determine the location of obstacles,obstacles, or other environmental features including traffic signs,trees, shrubs, neighboring vehicles, pedestrians, etc.

The autonomous driving module(s) 160 can be configured to receive,and/or determine location information for obstacles within the externalenvironment of the vehicle 100 for use by the processor(s) 110, and/orone or more of the modules described herein to estimate position andorientation of the vehicle 100, vehicle position in global coordinatesbased on signals from a plurality of satellites, or any other dataand/or signals that could be used to determine the current state of thevehicle 100 or determine the position of the vehicle 100 with respect toits environment for use in either creating a map or determining theposition of the vehicle 100 in respect to map data.

The autonomous driving module(s) 160 either independently or incombination with the 3D representation system 170 can be configured todetermine travel path(s), current autonomous driving maneuvers for thevehicle 100, future autonomous driving maneuvers and/or modifications tocurrent autonomous driving maneuvers based on data acquired by thesensor system 120, driving scene models, and/or data from any othersuitable source such as determinations from the sensor data 250.“Driving maneuver” means one or more actions that affect the movement ofa vehicle. Examples of driving maneuvers include: accelerating,decelerating, braking, turning, moving in a lateral direction of thevehicle 100, changing travel lanes, merging into a travel lane, and/orreversing, just to name a few possibilities. The autonomous drivingmodule(s) 160 can be configured can be configured to implementdetermined driving maneuvers. The autonomous driving module(s) 160 cancause, directly or indirectly, such autonomous driving maneuvers to beimplemented. As used herein, “cause” or “causing” means to make,command, instruct, and/or enable an event or action to occur or at leastbe in a state where such event or action may occur, either in a director indirect manner. The autonomous driving module(s) 160 can beconfigured to execute various vehicle functions and/or to transmit datato, receive data from, interact with, and/or control the vehicle 100 orone or more systems thereof (e.g., one or more of vehicle systems 140).

Detailed embodiments are disclosed herein. However, it is to beunderstood that the disclosed embodiments are intended only as examples.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims and as a representative basis for teaching one skilled in the artto variously employ the aspects herein in virtually any appropriatelydetailed structure. Further, the terms and phrases used herein are notintended to be limiting but rather to provide an understandabledescription of possible implementations. Various embodiments are shownin FIGS. 1-6, but the embodiments are not limited to the illustratedstructure or application.

The flowcharts and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flowcharts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved.

The systems, components and/or processes described above can be realizedin hardware or a combination of hardware and software and can berealized in a centralized fashion in one processing system or in adistributed fashion where different elements are spread across severalinterconnected processing systems. Any kind of processing system oranother apparatus adapted for carrying out the methods described hereinis suited. A typical combination of hardware and software can be aprocessing system with computer-usable program code that, when beingloaded and executed, controls the processing system such that it carriesout the methods described herein. The systems, components and/orprocesses also can be embedded in a computer-readable storage, such as acomputer program product or other data programs storage device, readableby a machine, tangibly embodying a program of instructions executable bythe machine to perform methods and processes described herein. Theseelements also can be embedded in an application product which comprisesall the features enabling the implementation of the methods describedherein and, which when loaded in a processing system, is able to carryout these methods.

Furthermore, arrangements described herein may take the form of acomputer program product embodied in one or more computer-readable mediahaving computer-readable program code embodied, e.g., stored, thereon.Any combination of one or more computer-readable media may be utilized.The computer-readable medium may be a computer-readable signal medium ora computer-readable storage medium. The phrase “computer-readablestorage medium” means a non-transitory storage medium. Acomputer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: a portablecomputer diskette, a hard disk drive (HDD), a solid-state drive (SSD), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), adigital versatile disc (DVD), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer-readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

Generally, modules as used herein include routines, programs, objects,components, data structures, and so on that perform particular tasks orimplement particular data types. In further aspects, a memory generallystores the noted modules. The memory associated with a module may be abuffer or cache embedded within a processor, a RAM, a ROM, a flashmemory, or another suitable electronic storage medium. In still furtheraspects, a module as envisioned by the present disclosure is implementedas an application-specific integrated circuit (ASIC), a hardwarecomponent of a system on a chip (SoC), as a programmable logic array(PLA), or as another suitable hardware component that is embedded with adefined configuration set (e.g., instructions) for performing thedisclosed functions.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber, cable, RF, etc., or any suitable combination ofthe foregoing. Computer program code for carrying out operations foraspects of the present arrangements may be written in any combination ofone or more programming languages, including an object-orientedprogramming language such as Java™ Smalltalk, C++ or the like andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer, or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

The terms “a” and “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising (i.e., open language). The phrase “at leastone of . . . and . . . ” as used herein refers to and encompasses anyand all possible combinations of one or more of the associated listeditems. As an example, the phrase “at least one of A, B, and C” includesA only, B only, C only, or any combination thereof (e.g., AB, AC, BC orABC).

What is claimed is:
 1. A system for generating 3D representations fromimages comprising: one or more processors; a memory communicably coupledto the one or more processors and storing: an image module includinginstructions that when executed by the one or more processors cause theone or more processors to: capture an image; a feature module includinginstructions that when executed by the one or more processors cause theone or more processors to: generate one or more feature maps for thecaptured image; a region-of-interest module including instructions thatwhen executed by the one or more processors cause the one or moreprocessors to: determine one or more regions-of-interests in thecaptured image based in part on the one or more feature maps; and alifting module including instructions that when executed by the one ormore processors cause the one or more processors to: for eachregion-of-interest, generate a 3D representation based on theregion-of-interest and the one or more feature maps.
 2. The system ofclaim 1, wherein each region-of-interest corresponds to a vehicle. 3.The system of claim 1, further comprising a training module includinginstructions that when executed by the one or more processors cause theone or more processors to: generate training data from the generated 3Drepresentations.
 4. The system of claim 1, wherein each 3Drepresentation comprises a 3D box.
 5. The system of claim 1, whereineach 3D representation is generated using a lifting function.
 6. Thesystem of claim 5, further comprising a loss module includinginstructions that when executed by the one or more processors cause theone or more processors to calculate a loss for one or more the 3Drepresentations.
 7. The system of claim 6, wherein the loss modulefurther includes instructions that when executed by the one or moreprocessors cause the one or more processors to train the liftingfunction using the calculated loss.
 8. The system of claim 1, whereingenerating a 3D representation based on the region-of-interest and theone or more feature maps comprises: calculating a height and width forthe region-of-interest from the one or more feature maps; calculating ageometric center for the region-of-interest from the one or more featuremaps; calculating a camera distance for the region-of-interest from theone or more feature maps; calculating a rotation for theregion-of-interest from the one or more feature maps; and generating the3D representation of the region-of-interest from one or more of thecalculated height and width, the calculated geometric center, thecalculated camera distance, and the calculated rotation.
 9. The systemof claim 1, wherein the image is a 2D monocular image.
 10. A method forgenerating 3D representations from images, the method comprising:receiving an image and one or more feature maps for the image;determining one or more regions-of-interest in the image based in partof the image and the one or more feature maps; for eachregion-of-interest, generating a 3D representation based on theregion-of-interest and the one or more feature maps using a liftingfunction; calculating a loss for one or more of the generated 3Drepresentations; and training the lifting function using the calculatedloss.
 11. The method of claim 10, wherein each region-of-interestcorresponds to a vehicle.
 12. The method of claim 10, further comprisinggenerating training data from the generated 3D representations.
 13. Themethod of claim 10, wherein each 3D representation comprises a 3D box.14. The method of claim 10, wherein generating a 3D representation basedon the region-of-interest and the one or more feature maps using thelifting function comprises: calculating a height and width for theregion-of-interest from the one or more feature maps; calculating ageometric center for the region-of-interest from the one or more featuremaps; calculating a camera distance for the region-of-interest from theone or more feature maps; calculating a rotation for theregion-of-interest from the one or more feature maps; and generating the3D representation of the region-of-interest from one or more of thecalculated height and width, the calculated geometric center, thecalculated camera distance, and the calculated rotation.
 15. The methodof claim 10, wherein the image is a 2D monocular image.
 16. Anon-transitory computer-readable medium for generating 3Drepresentations from images and including instructions that whenexecuted by one or more processors cause the one or more processors to:capture an image; generate one or more feature maps for the capturedimage; determine one or more regions-of-interest in the image based inpart of the image and the one or more feature maps; and for eachregion-of-interest, generate a 3D representation based on theregion-of-interest and the one or more feature maps using a liftingfunction.
 17. The non-transitory computer-readable medium of claim 16,wherein each region-of-interest corresponds to a vehicle.
 18. Thenon-transitory computer-readable medium of claim 16, wherein each 3Drepresentation comprises a 3D box.
 19. The non-transitorycomputer-readable medium of claim 16, wherein generating a 3Drepresentation based on the region-of-interest and the one or morefeature maps comprises: calculating a height and width for theregion-of-interest from the one or more feature maps; calculating ageometric center for the region-of-interest from the one or more featuremaps; calculating a camera distance for the region-of-interest from theone or more feature maps; calculating a rotation for theregion-of-interest from the one or more feature maps; and generating the3D representation of the region-of-interest from one or more of thecalculated height and width, the calculated geometric center, thecalculated camera distance, and the calculated rotation.
 20. Thenon-transitory computer-readable medium of claim 16, wherein the imageis a 2D monocular image.